Abstract

Enterprise2.0 is the use of emergent social software tools to improve knowledge sharing and collaboration within and between firms, their customers and partners. This paper proposes that Enterprise2.0 is a double‑edged sword and should be adopted cautiously. Emerging trends in e‑business are specialisation and collaboration, creating a diverse population of organisations, each tightly defined by its core competences, interacting in a constant sequence of transient relationships, each motivated by a particular market opportunity. These dynamic business networks depend on the establishment of appropriate platforms and global standards to enable smooth interaction between the service components, in particular, appropriate metadata such as ontologies. The dynamism of such an interconnected yet free‑ wheeling economy is constrained unless risks relating to investment in a new business relationship are reduced to levels where the risk‑reward ratio favours agility rather than inertia. For its advocates, Enterprise2.0 techniques promise to contribute to the evolution of dynamic, agile, collaborative e‑commerce. However, its egalitarian and permissive nature creates challenges. Folksonomies allow a more customer‑centric view of an organisation's value proposition but may also undermine carefully devised official ontologies. Collaborative filtering may provide a mechanism for mitigating risk but the trust created is dependent upon the perceived credibility of the reviewers. A high profile example of an initiative designed to facilitate dynamic e‑commerce which failed due to unsatisfactory classification of its members and the perceived risk of interacting with unknown reputations is examined. Recent academic research and practical applications that address these conflicts are reviewed.

Abstract

As technology transforms knowledge and information management systems, statistical data is becoming more accessible, available in bigger and more complex datasets and is able to be analysed and interpreted in so many different ways. Traditional approaches to the development, maintenance and revision of statistical classifications no longer support or enable description of data in ways that are as useful to users as they could be. The ability to search and discover information in ways that were previously not possible means that new methodologies for managing and describing the data, and its associated metadata, are required. The development of structured lists of categories, often hierarchic in nature, based on a single concept, limited by the constraints of the printed page, statistical survey processing system needs, sequential code structures or narrow user defined scopes results in statistical classifications neither dynamically reflecting the real world of official statistics nor maintaining relevance in a fast changing information society. Opportunities exist for modernising the developmental processes for statistical classifications by using, for example, semantic web technology, Simple Knowledge Organisation Systems (SKOS), and Resource Description Frameworks (RDF), and for better describing metadata and information within and across multiple, interconnected information and knowledge management systems. These opportunities highlight the difficulties that come with using traditional approaches to statistical classification development and management, and encourage new thinking for different and more flexible options for developers and users. This paper explores the need to dispense with traditional practices for developing statistical classifications as cornerstones of metadata, knowledge and information management, and comments on the need to change the underlying methodology within statistical classification theory, best practice principles and how they can be used in associated information management systems.